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OPEN Reveals of candidate active ingredients in Justicia and its anti‑thrombotic action of mechanism based on network pharmacology approach and experimental validation Zongchao Hong1,5, Ting Zhang2,5, Ying Zhang1, Zhoutao Xie1, Yi Lu1, Yunfeng Yao1, Yanfang Yang1,3,4*, Hezhen Wu1,3,4* & Bo Liu1,3*

Thrombotic diseases seriously threaten human life. Justicia, as a common Chinese medicine, is usually used for anti-infammatory treatment, and further studies have found that it has an inhibitory efect on platelet aggregation. Therefore, it can be inferred that Justicia can be used as a therapeutic drug for thrombosis. This work aims to reveal the pharmacological mechanism of the anti-thrombotic efect of Justicia through network pharmacology combined with wet experimental verifcation. During the analysis, 461 compound targets were predicted from various databases and 881 thrombus-related targets were collected. Then, herb-compound-target network and protein–protein interaction network of disease and prediction targets were constructed and cluster analysis was applied to further explore the connection between the targets. In addition, Gene Ontology (GO) and pathway (KEGG) enrichment were used to further determine the association between target proteins and diseases. Finally, the expression of hub target proteins of the core component and the anti-thrombotic efect of Justicia’s core compounds were verifed by experiments. In conclusion, the core bioactive components, especially justicidin D, can reduce thrombosis by regulating F2, MMP9, CXCL12, MET, RAC1, PDE5A, and ABCB1. The combination of network pharmacology and the experimental research strategies proposed in this paper provides a comprehensive method for systematically exploring the therapeutic mechanism of multi-component medicine.

Te advancement of medical technology and the efective control of fatal diseases have greatly increased the average life span of human beings. Trombotic diseases, including pulmonary embolism, venous embolism, arterial embolism, cerebral embolism, etc., have seriously threatened human life. Sadly, the global public aware- ness of thrombotic diseases, especially pulmonary embolism and deep vein thrombosis, is not high. What needs more vigilance is that thromboembolic conditions have been estimated to account for 1 in 4 deaths worldwide in 2010­ 1. As a threatening disease, thrombosis can be accompanied by ­cancer2, obesity, and infammatory bowel disease­ 3. Clinical data of patients with coronavirus disease (COVID-19) in 2019 also reported high venous thromboembolism and difuse intravascular coagulation, and thrombotic complications even became a sign of severe COVID-194,5. Te etiology and pathogenesis of thrombotic diseases are very complex and have not yet been fully clarifed. However, recent studies have shown that the occurrence and development of thrombotic diseases are mainly

1Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan 430065, China. 2Afliated Hospital of Hubei University of Chinese Medicine, Hubei Hospital of Traditional Chinese Medicine, Wuhan, China. 3Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Wuhan, China. 4Collaborative Innovation Center of Traditional Chinese Medicine of New Products for Geriatrics Hubei Province, Wuhan, China. 5These authors contributed equally: Zongchao Hong and Ting Zhang. *email: [email protected]; [email protected]; [email protected]

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related to fve reasons: (1) Vascular endothelial damage­ 6; (2) Increased platelet count­ 7; (3) Increased blood ­coagulation8; (4) Decreased anti-coagulant ­activity9; (5) Decreased fbrinolytic ­activity10. At present, the treat- ments for thrombotic diseases mainly includes anti-coagulation therapy, anti-platelet drug therapy, thrombolytic therapy, interventional therapy, and surgery­ 11. However, currently commercially available anti-thrombotic drugs do not seem to be fully applicable to all patients. Terefore, the development and use of new anti-thrombotic drugs is inevitable. Natural products, as a treasury for the development and discovery of new medicines, are worthy of our in- depth exploration. Te discovery of anti-thrombotic active ingredients from natural products can efectively enrich the drug bank for the treatment of thrombosis. Justicia (Justicia procumbens (L.) Ness), a resource-rich botanical, has been found to have a variety of pharmacological activities including anti-infammatory, liver protection, anti-oxidation, and anemia ­treatment12–14. Even more surprising is that we accidentally discovered its anti-platelet aggregation ability­ 15. A further conjecture is that the Justicia has a satisfactory anti-thrombotic efect or can assist the treatment of thrombotic diseases. However, the research evidence on the anti-thrombotic efect of Justicia is scarce, so that we do not know its anti-thrombotic mechanism. Te network pharmacology proposed by Hopkins University in 2008, is a big data integration method based on a large number of database resources and statistical algorithms. It is used to explore the synergy of multiple components, multiple targets, and multiple mechanisms for the treatment of diseases. From this point of view, the research strategy of network pharmacology is actually consistent with the theory of Chinese medicine. Net- work pharmacology is a new feld based on the development of "ingredient-target-disease" interaction network multidisciplinary integration theory, which provides a new way for modern medicine to explain the responsibility system of Chinese medicine, which also provides a novel strategy for our research purposes­ 16. Traditional Chi- nese Medicine (TCM) treats individuals or patients as systems in diferent states and has accumulated numerous herbal prescriptions. Te overall concept of TCM has a lot in common with the core ideas of emerging network pharmacology and network biology. Based on the existing network pharmacology technology, it is necessary to establish a new TCM network pharmacology method to predict the target spectrum of herbal compounds and pharmacological efects, reveal the drug-gene-disease co-module association, screen synergistic multiple compounds from herbal formulas in a high-throughput manner, and explain the combination rules and network regulation mode of action. Furthermore, the combination of computing, experimentation, and clinical practice is a promising strategy and direction for the future research of TCM network ­pharmacology17,18. In this work, we frst used network pharmacology to analyze the active components and signaling pathways of the anti-thrombotic efect of Justicia, and then verifed the results using component analysis, in vivo and in vitro experiments, and GeneChip Human Gene Array. Materials and methods Chemical database collection. All components of Justicia were obtained from A high-throughput experiment- and reference-guided database of traditional Chinese medicine (HERB, http://​herb.​ac.​cn) and Te PubChem Project (https://​pubch​em.​ncbi.​nlm.​nih.​gov)19,20. HERB, a high-throughput experiment and reference guide database for traditional Chinese medicine, associates 12,933 targets and 28,212 diseases with 7263 herbs and 49,258 components, and provides six paired relationships in HERB, which will greatly support the moderni- zation of traditional Chinese medicine and guide reasonable modern drug discovery.

Compounds target fshing. As described by Hsin-Yi Lin et al.21, we obtained the SMILES format of Jus- ticia’s ingredients from the PubChem database and fshed out the potential targets of compounds from HitPick (http://​mips.​helmh​oltz-​muenc​hen.​de/​hitpi​ck/), similarity ensemble approach (SEA, http://​sea.​bkslab.​org/), Tar- get Hunter of Small Molecule (https://​www.​cblig​and.​org/​Targe​tHunt​er/) database. Ten, we integrated the tar- gets that were caught and removed duplicates (ensure the uniqueness of each target).

Targets of thrombus. Te thrombus targets were gathered from the GeneCards database (https://​www.​ genec​ards.​org/)22, which provides information about disease targets. Te keywords “thrombus” were used, and a total of 881 targets were gathered.

Obtaining protein–protein interaction data. Te targets to be analyzed were imported into the STRING database (https://​string-​db.​org/) to obtain the protein–protein interaction (PPI) relationship, and the obtained PPI data was exported for network visualization.

Network construction and analysis. All networks were constructed in Cytoscape 3.8.1 sofware (https://​ cytos​cape.​org/​downl​oad.​html), and the visualization network is composed of nodes and edges. Nodes represent components, targets, and herbs. To study further into the network, MCODE and Cytohubba plugin were introduced to generate clusters and identify hub nodes. Te MCODE plugin can determine the main center of the network and generate clusters of interconnected sub-clusters by setting K-cores. Increasing the value of K-core will generate fewer clusters and exclude smaller clusters. Moreover, we can fnd the node with the highest score, called SEEDs, which may have the opportunity to become the key target of this cluster. Hub genes are usually representative genes. Cytohubba is a plugin used by Cytoscape sofware to identify hub nodes. Te larger the score of the analysis result, the more critical the node and the darker the color displayed.

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Gene ontology and KEGG enrichment analysis. Gene Ontology (GO) enrichment analysis is to show the biological process (BP), cellular component (CC), and molecular function (MF) that genes afect together. Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis can discover the importance of signaling pathways involved in genes. Metascape (http://​metas​cape.​org/) is a powerful gene func- tion annotation analysis tool. It integrates multiple authoritative data resources such as GO, KEGG, UniProt, and DrugBank, so that it can not only complete pathway enrichment and biological process annotation, but also do gene-related protein network analysis and predictive analysis of transcription regulation ­involved23. Terefore, we employed metascape for the enrichment analysis of this study.

Reagents and materials. DMSO (#30072418), NaCl (#10019318), ­KH2PO4 (#10017618), ­Na2HPO4·12H2O (#2006062), and KCl (#130412) are all analytical pure, provided by Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China); ADP (#11607920, Sigma, USA), Aspirin (#A2093, Sigma, USA), TRIzol (#15596026, Termo Fisher Scientifc, USA); water was prepared by Millipore Milli-Q; Justicidin D was purifed by the laboratory (purity > 95%, the results of 1H-NMR identifcation are shown in Figure S1 in the support material). Human– machine platelet collection is provided by Wuhan Blood Center (blood for scientifc research, approved by Hubei Provincial Blood Management Center, Wuhan, China). RNA extraction kit (QIAGEN, Germany), Pico Reagent Kit and GeneChip Human Gene 1.0 ST Array (Afymetrix, USA). GeneChip scanner (30007G, Afym- etrix, USA). Rat tail tendon collagen type I (#C8062) was purchased from solarbio (Beijing, China). Adrenalin hydrochloride was purchased from Tianjin Jinyao Pharmaceutical Co., Ltd. (Tianjing, China). PBS: 8 g NaCl, 0.2 g KCl, 1.44 g ­Na2HPO4, and 0.24 ­gKH2PO4, dissolved in 900 ml ultra pure water, adjust the pH to 7.4 with hydrochloric acid, add water to 1000 ml, and autoclave for 20 min. Justicidin D was prepared into 6.8 mg/ml stock solution in DMSO and diluted to 0.34 mg/ml with PBS. ADP could induce platelet aggregation, it was prepared as a 0.12 mg/ml solution with PBS when used. Aspirin was formulated with PBS as a stock solution at a concentration of 10 mM.

Transcriptional sequencing. Te RNA expression of platelets was analyzed in blank group (treatment with 5% DMSO 45 μl and PBS 15 μl) and justicidin D group (treatment with 15 μl of ADP solution and 45 μl of justicidin D solution). Each group was reacted in a water bath at 37 °C for 5 min, and then centrifuged at 3000 rpm for 3 min. Afer repeated washing and centrifugation with PBS for 2 times, 2 ml of TRIzol was added for total RNA extraction. Follow the instructions of the RNA extraction kit to extract platelet RNA. Afer quality testing, high-quality samples (OD260/280 = 1.8–2.2, OD260/230 ≥ 2.0, RIN ≥ 6.5, 28S:18S ≥ 1.0) were used to construct sequencing libraries. Ten, cDNA reverse transcription synthesis was performed according to the kit instructions. Briefy, double-stranded cDNA was synthesized using the superscript double-stranded cDNA synthesis kit (Pico Reagent Kit) and random hexamer primers. According to the Illumina library construction program, the synthesized cDNA was repaired, phosphorylated, and added ’A’ base. Te size of the library was 200–300 bp, and it was screened with 2% low-range ultra-agarose, followed by PCR amplifcation. Afer TBS380 quantifcation, the paired-end RNA seq sequencing library was used to hybridize with the GeneChip Human Gene 1.0 ST Array chip. Finally, get the scanned data.

Diferential expression analysis. To identify diferential expression genes (DEGs) between two difer- ent group samples, Te R statistical package sofware EdgeR (http://​www.​bioco​nduct​or.​org/​packa​ges/2.​12/​bioc/​ html/​edgeR.​html) was used to assess diferential expression. Afer corrected for multiple hypothesis testing, |Fold change|> 1, and p < 0.05 was the threshold for screening DEGs. Hiplot (https://​hiplot.​com.​cn/​basic) and bioinformatics (http://​www.​bioin​forma​tics.​com.​cn/) was used for visualization of results.

Anti‑platelet aggregation. Turbidimetric platelet aggregometry (TPA) is the most widely used and clas- sical method for clinical determination of platelet aggregation rate­ 24. Take machine-collected platelets, centri- fuge at 800 rpm for 8 min, and suck the upper layer of platelets for the experiment. Grouped into: blank group, ADP group, justicidin D group, and aspirin group (Positive control). Te blank group was treated with 5% DMSO solution (diluted with PBS), and the ADP group was the model group. In addition to ADP treatment, justicidin D group and aspirin group also gave justicidin D and aspirin treatment, respectively. Percentage of platelet aggregation: Take 260 μl of platelets (3 × ­108·ml−1) and store them in a reaction cup preheated at 37 °C with a magnetic rod. Add 30 μl of the drug solution and incubate for 25 min. While magnetic stirring, add ADP inducer 10 μl. Te platelet-removed plasma reaction tube set in parallel was used for zero adjustment, and the platelet-rich reaction tube was adjusted to 100%, and the light transmittance of each tube was measured to record the maximum platelet aggregation rate within 5 min. In the blank group, 30 μl of 5% DMSO solution was used to replace the drug solution to perform the same operation. Platelet aggregation inhibition rate (%) = (1–(maximum aggregation percentage of experimental group / maximum aggregation percentage of blank group)) × 100% (n = 3).

Animals. 40 male Balb/c mice (SPF grade) weighing 18–22 g were provided by the Experimental Animal Center of China Tree Gorges University. Randomly divided into 4 groups (blank group, model group, aspirin group, justicidin D group), 10 animals in each group, reared in a standard environment, environment tempera- ture 22 °C, relative humidity 30–50%, 12 h light–dark cycle. Mice in each group were intragastrically adminis- tered with the corresponding concentration of medicinal solution for 7 consecutive days. Te mice were fasted 12 h before the last administration, and the models were made 1 h afer the last administration. Rat tail tendon collagen type I (500 μg/ml) and adrenalin hydrochloride (15 μg/ml) mixture (v:v = 1:1) were injected into the tail

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vein of the model group and the administration group, and the blank group was injected into the tail vein with equal volume of saline. Te volume of intragastric administration is 0.2 ml/10 g body weight, and the volume of tail vein injection is 0.1 ml/10 g body weight. Observe the physiological behavior of the mice within 30 min afer modeling, and calculate the protection rate. Take lung slices for HE staining to observe thrombosis. Pro- tection rate% = (number of surviving mice in the drug treatment group/number of surviving mice in the blank group) × 100%. Animal welfare and experimental procedures strictly follow the guidelines of the Animal Research Committee of Hubei University of Chinese Medicine, comply with the European Community guidelines (EEC Directive of 1986; 86/609/EEC), and carried out in compliance with the ARRIVE guidelines.

Statistical analysis. Student’s t-test was used to compare gene expression between diferent groups. Te statistical value p < 0.05 indicates that the diference is statistically signifcant. Graphpad Prism 8.0 was also used for statistical analysis.

Statement. Animal experimental research has been approved by the Animal Research Committee of Hubei University of Chinese Medicine. Animal welfare and experimental procedures strictly follow the guidelines of the Animal Research Committee of Hubei University of Chinese Medicine, comply with the European Commu- nity guidelines (EEC Directive of 1986; 86/609/EEC), and carried out in compliance with the ARRIVE guide- lines.

Informed consent. Te informed consent of the blood contributor was obtained, and protocols were approved by the ethics committee of Hubei Provincial Hospital of Traditional Chinese Medicine. Te research process followed the Declaration of Helsinki made by the World Medical Association (WMA). Results Components of Justicia and targets. We collected 39 Justicia candidates from HERB database for fur- ther analysis. At the same time, we predicted the possible targets of each compound and obtained a total of 460 single targets (Supplementary Table S1). Notably, the same compound has multiple targets, and diferent compounds may also act on the same target, which conforms to the characteristics of multi-component and multi-target of botanicals. 881 genes related to thrombus were collected from the GeneCards database, and we used the top 400 genes for the next analysis.

Topology network construction. 39 compounds were collected in Justicia, and 460 possible targets were predicted, so we used Cytoscape sofware to construct the herb-compound-target topology network (Fig. 1A). Te network contains a total of 500 nodes (one herb, 39 compounds, 460 targets), 1402 edges. Among these com- ponents, palmitic acid, quercetin, justin B, and justin A have the highest number of targets. Correspondingly, EDNRB, EDNRA, and NROB1 are the common targets of more than half of the compounds. In depth, we use the cytohubba plug-in to analyze the topological network and get the core compounds and the corresponding targets (Fig. 1B), and the top 20 nodes are shown in Table 1. Undoubtedly, palmitic acid, quercetin, Justin B, and Justin A are at the top of the list. What is more surprising is that justicidin E and justicidin D, the characteristic ingredients of Justicia, are among the top 15 compounds. Under the premise of analyzing the anti-thrombotic efect of Justicia, we included 400 targets related to thrombus into the analysis process. Among these 400 targets, 48 were predicted by 34 Justicia compounds (It can be called common targets, shown in Supplementary Table S2, Fig. 1C). Similarly, we constructed a topological network of 34 compounds and 48 shared targets (Fig. 1D). Tere were 224 edges in this network, which means that there were 224 connections between 34 compounds and 48 targets. Not surprisingly, under Cytohubba’s analysis, justicidin D remains one of the core nodes. In addition, nine targets, TNFRSF1A, RAC1, ABCG2, IL2, P4HB, CYP2C19, ABCB1, ERAP1, and PDE5A, also appeared in the top 20 core nodes (Fig. 1E, Table 2). In-depth understanding is that these nine targets may play a vital role in the anti-thrombotic efect of Justicia.

Prediction targets PPI network construction. Te compounds in Justicia predicted 460 targets. Te symbols of these targets were input into STRING database, species were limited to “Homo sapiens”. Ten, it was imported into Cytoscape3.8.1 to construct the network (Fig. 2A). Afer PPI was acquired, cytohubba calculated the top 60 target networks (Fig. 2B), among which MT-ND5, MT-ND2, MT-ND4, NDUFS2, and NDUFA8 ranked in the top fve.

Clusters of prediction target PPI network. MCODE plug-in is used for cluster analysis. Six clusters were obtained afer conducting clustering analysis for the prediction target network, and each cluster has seed nodes (marked by a blue circles). Te details are shown in Fig. 3 and Table 3. Cluster 1 contains 49 nodes and 1172 edges with a score of 48.83. Te seed node of cluster 1 is NDUFA4, which is a cytochrome c oxidase subunit and has been confrmed that its mutation can cause human diseases, especially nervous system diseases, and is associated with immune ­response25,26. Cluster 2 contains 29 nodes and 406 edges, with a score of 29.0. Te seed node is S1PR2, which plays a vital role in atherosclerosis and can be used as a novel therapeutic target for atherosclerosis. In addition, S1PR2 has been proven to enhance endothelial barrier function in vivo and in vitro, and participate in platelet aggregation­ 27,28.

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Figure 1. Constructed network related to Herb-compounds-targets. (A) Herb-compounds-predicted targets network. (B) Te core components in the Herb-compound-predicted target network calculated by Cytohubba (top 20). (C) Te intersection of justicia’s compound predicted targets and thrombus-related targets, with 48 common targets. (D) Herb-compounds-common targets network. (E) Te 11 core components and 9 hub targets in the herb-compounds-common targets network calculated by Cytohubba. Te purple square represents the botanical justicia, the three-pointed star represents the compound, and the circle represents the target. For the targets in (A,D), the darker the circle, the larger the degree. Te light yellow to dark orange gradient in (B,E) indicates that the corresponding node is more important.

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Rank Name Score PubChem CID 1 Palmitic acid 117 985 2 Quercetin 98 5,280,343 3 Justin B 89 10,672,178 4 Justin A 84 10,551,264 5 Apigenin 82 5,280,443 6 Luteolin 80 5,280,445 7 Scopoletin 70 5,280,460 8 Dihydroclusin diacetate 68 21,600,064 9 Cynaroside 64 5,280,637 10 Rhoifolin 52 5,282,150 11 Kaempferol 48 5,280,863 12 Stigmasterol 45 5,280,794 13 Justicia 39 N/A 14 Diphyllin 33 100,492 15 Justicidin E 32 363,128 16 Justicidin D 32 5,318,737 17 Chinensinaphthol 30 332,529 18 Taiwanin E 29 493,164 19 Taiwanin C 28 363,127 20 Cleistanthin B 28 119,458

Table 1. Top 20 nodes in Merged Network constructed by Herb-Compounds-Prediction targets.

Rank Name Score 1 Justicia 34 2 Quercetin 19 3 Apigenin 18 4 TNFRSF1A 15 5 Luteolin 14 6 Cynaroside 13 7 RAC1 13 8 ABCG2 12 9 IL2 12 10 P4HB 11 11 Scopoletin 10 12 Palmitic acid 10 13 CYP2C19 10 14 ABCB1 10 15 Chinensinaphthol methyl ether 8 16 Cleistanthin B 8 17 Diphyllin 8 18 ERAP1 8 19 PDE5A 8 20 Justicidin D 7

Table 2. Top 20 nodes in the network constructed by Herb-Compounds-Common targets.

Cluster 3 contains 18 nodes and 99 edges, with a score of 12.24. Te seed node is RELA, which mediates the reduction of atherosclerotic plaque in the aortic valve, and targeting RELA can inhibit vascular endothelial ­dysfunction29,30. Cluster 4 contains 26 nodes and 153 edges, with a score of 11.65. Te seed node is F2 (Prothrombin), which functions in blood coagulation, and an unexplainable novel F2 gene mutation for thrombosis was discovered in a Dutch white family­ 31. Cluster 5 contains 37 nodes and 170 edges, with a score of 9.44. Te seed node is MMP9, and its protein content and enzyme activity in platelets of myocardial infarction are signifcantly increased. Furthermore, the overexpression of MMP9 can mediate the pro-angiogenic function in tumors­ 32,33.

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Figure 2. Network analysis on compound predicted targets. (A) Te protein–protein interaction (PPI) network of 460 targets predicted by compounds. Te closer, redder and the larger the nodes are, the higher the degree of freedom they have. (B) Important nodes in PPI network (top 60, calculated by cytohubba), the darker the nodes, the higher their importance.

Figure 3. Clusters of compound predicted targets PPI network. (A–F) are clusters we found in the predicted targets PPI network which stands for cluster 1 to 6, respectively. Te seed node of each clusters is marked with blue circles.

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Cluster Score Nodes Edges Gene IDs NDUFS2, NDUFA8, NDUFA3, NDUFA1, NDUFA11, NDUFA13, NDUFB2, NDUFC1, NDUFAF4, MT-ND6, NDUFC2, NDUFA4, NDUFB1, NDUFV2, NDUFV1, NDUFS8, NDUFA7, NDUFB8, NDUFAF2, NDUFB11, NDUFS6, NDUFA9, NDUFS3, NDUFAF1, NDUFS7, NDUFB7, MT-ND1, 1 48.83 49 1172 NDUFS5, NDUFB4, NDUFS1, NDUFB10, MT-ND3, NDUFAF3, NDUFA5, NDUFV3, NDUFS4, NDUFA10, NDUFB5, NDUFB3, NDUFA6, NDUFB6, NDUFA12, NDUFAB1, NDUFA2, NDUFB9, MT-ND5, MT-ND2, MT-ND4, MT-ND4L GNAI1, S1PR2, CCR4, S1PR5, OXER1, CHRM2, CXCR1, S1PR4, LPAR2, LPAR3, APPG, ABBR1, 2 29.00 29 406 SSTR3, MTNR1A, CXCL12, TAS1R3, TAS1R1, DRD4, TAS2R31, S1PR1, GPR18, S1PR3, LPAR1, GABBR2, GNAI3, SSTR1, NMUR2, MCHR1, PTGER3 GSK3B, MMP3, HRAS, IGF1R, ESR1, MMP13, TLR2, FGF1, PTGS2, MMP2, TNFRSF1A, IGFBP3, 3 12.24 26 153 EGFR, KDR, EP300, AHR, PGR, HDAC1, PPARG, REN, MET, PARP1, RELA, TLR1, AR, F3 FOS, P2RY10, F2, PIK3R1, NFKB1, FFAR4, TBXA2R, LPAR4, EDNRA, LPAR6, EDNRB, PTGFR, 4 11.65 18 99 VEGFA, LTB4R, TNF, HTR2B, CHRM3, CREB1 EPHB6, EPHB3, NFE2L2, LDLR, TERT, CYP2J2, EPHA2, ALOX15, PTPN1, PLA2G2A, EPHB1, PLA2G4B, EPHB2, PLA2G5, EPHA1, SELP, EPHA8, IL6, ALOX12, EPHA5, TH, CYP2C19, 5 9.44 37 170 CYP2C8, PGF, IL1B, MIF, MPO, EPHA4, FGF2, EPHA3, EPHA6, EPHA7, LGALS3, IL2, SHH, MMP9, GLI1 PRKCA, PTK2, DPP4, GSR, GSTA1, GSTO1, LGALS4, HDAC3, PTGER4, PLA2G10, AKT1, PLA2G2C, CYP4F2, NOX4, HSD17B3, IKBKG, ENSG00000196689, PLCG1, FLT3, SELL, CYP2D6, 6 5.03 36 88 HSD17B2, PTPN6, CYP1A2, ELAVL1, CD38, GSTM1, UGT2B7, KIT, ACHE, PRF1, SRD5A1, NRAS, PPARA, EPHX1, RAC1

Table 3. Clusters of the predicted target PPI network.

Cluster 6 contains 36 nodes and 88 edges, with a score of 5.03. Te seed node is SELL, which can be used as an indicator to predict myocardial infarction and mediate the interaction between tumor cells and blood com- ponents (including platelets, endothelial cells, and white blood cells)34,35.

Disease PPI network construction. Based on the results of the database GeneCards, there were a total of 881 candidate targets related to thrombosis. Import these target genes into STRING to get PPI network data. Ten, the frst 400 targets were imported into Cytoscape 3.8.1 to visualize the network (Fig. 4A). Te Maximal Clique Centrality (MCC) algorithm based on Cytohubba calculates the frst 60 nodes in the network (Fig. 4B), among which IL6, IL10, CXCL8, IL4, ICAM1 were the most important top 5 nodes.

Clusters of disease PPI network. Cluster analysis of thrombus-related gene PPI network, 4 clusters were obtained. Te detailed information are shown in Fig. 5 and Table 4. Cluster 1 contains 64 nodes and 1774 edges, with a score of 56.32. Te seed node of this cluster is CXCL12, which promotes the uptake of platelet oxidixed low-density lipoprotein (oxLDL) and synergistically enhances the efects of LDL-oxLDL-induced pro-oxidation and pro-thrombosis on platelet ­function36. Cluster 2 contains 38 nodes and 185 edges, with a score of 10.00. Te seed node of this cluster is MET (also called HGF), which is an angiogenic factor and a therapeutic target for a variety of solid tumors. Te increase in its concentration is also a sign of arterial thrombosis­ 37,38. Cluster 3 contains 34 nodes and 148 edges, with a score of 8.97. Te seed node of this cluster is FCGR2A (also known as FcγRIIA), which is expressed in human platelets. Te expression of FcγRIIA by transgenic technology in lupus mice can trigger major changes in the platelet transcriptome and cause lung and kidney ­thrombosis39. Cluster 4 contains 26 nodes and 104 edges, with a score of 8.16. Te seed node of this cluster is PPBP, which is a biomarker of platelet degranulation, and plasma PPBP can activate ­coagulation40.

Enrichment analysis. In the network construction stage, 48 genes were found to exist in both thrombosis- related genes and compound prediction genes, and Fig. 1E shows the core 9 of the 48 genes. Terefore, we imported 48 common genes and 9 key genes into the Metascape database for enrichment analysis and selected the top 10 of each category for display. For the enrichment results of 48 genes, the biological process (BP) is shown in Fig. 6A, including regulation of leukocyte cell–cell adhesion (GO ID: 1903037), regulation of infammatory response (GO ID: 0050727), regulation of cell adhesion (GO ID: 0030155), and regulation of response to external stimulus (GO ID: 0032103). Cellular component (CC) is shown in Fig. 6B, including side of membrane (GO ID: 0098552), external side of plasma membrane (GO ID: 0009897), apical part of cell (GO ID: 0045177), cytoplasmic side of membrane (GO ID: 0098562), and extrinsic component of membrane (GO ID: 0019898). Molecular function (MF) as shown in Fig. 6C, includes cytokine receptor binding (GO ID: 0005126), cytokine activity (GO ID: 0005125), protein homodimerization activity (GO ID: 0042803), and kinase activity (GO ID: 0016301). In addition, KEGG is shown in Fig. 6D, including MAPK signaling pathway (KEGG ID: hsa04010), Cytokine-cytokine receptor interaction (KEGG ID: hsa04060), T17 cell diferentiation (KEGG ID: hsa04659), and JAK-STAT signaling pathway (KEGG ID: hsa04630). Te details of the above enriched entries are described in Supplementary Table S3. For the enrichment results of 9 core genes (described in Supplementary Table S4), the BP, CC, and MF are shown in Fig. 7A. It is not difcult to fnd that compared with the enrichment results of 48 genes, there are simi- larities in cell adhesion, drug transport, membrane function, and molecular function. For the KEGG enrichment analysis (Fig. 7B shows the frst 10 entries), we were not surprised to fnd in the two enrichment results that 5 of the frst 20 entries overlapped, including PI3K-Akt signaling pathway, fuid shear stress and atherosclerosis,

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Figure 4. Network analysis on thrombus-related targets. (A) Te PPI network of the top 400 thrombus- related targets. Te closer, redder and the larger the nodes are, the higher the degree of freedom they have. (B) Important nodes in PPI network (top 60, calculated by cytohubba), the darker the nodes, the higher their importance.

human cytomegalovirus infection, MAPK signaling pathway, and Cytokine-cytokine receptor interaction, these signaling pathways are directly or indirectly related to thrombosis. Interestingly, the seed nodes F2, CXCL12, MMP9, and MET in the disease target and compound prediction target network clusters contribute to the enrich- ment of these fve signaling pathways (Fig. 8A, Table 5). Tis gives us confdence to determine that the core node analysis for the compounds in Justicia and thrombus-related targets is instructive. Core compounds including justicidin D may regulate the expression of F2, CXCL12, MMP9, and MET.

Justicidin D causes diferential expression of thrombus‑related genes. In view of the core analy- sis of the compound and target network, justicidin D, as one of the core components, proved to be a unique active component in Justicia. Terefore, we conducted an experimental studies on justicidin D.

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Figure 5. Clusters of thrombus-related targets PPI network. (A–D) are clusters we found in the thrombus- related targets PPI network which stands for cluster 1 to 4, respectively . Te seed node of each clusters is marked with blue circles.

Cluster Score Nodes Edges Gene IDs CRP, FOXP3, TLR4, TLR2, CXCL12, SRC, JUN, FASLG, CD40, CCL2, PTGS2, SELL, MPO, CD40LG, IFNG, TGFB1, IL17A, MMP2, CASP3, CD34, CSF3, FCGR2B, TLR9, ANXA5, GZMB, ALB, STAT1, NOTCH1, FAS, TP53, TNFRSF1A, SELE, STAT3, JAK2, ICAM1, SELP, IL2, 1 56.32 64 1774 NLRP3, CD86, CXCL8, MMP9, CXCR4, HMGB1, ITGAM, TLR5, PRF1, CXCL10, CTLA4, IL10, CSF2, FN1, IL3, IL4, IL18, VEGFA, AKT1, TNF, CD274, CD44, TLR7, IL6, IL1B, IL7, MAPK8 PROS1, PDGFRA, PLAU, F5, FYN, CTNNB1, MET, GAS6, SERPINF2, ANGPT1, FGA, CD47, FGB, CYCS, ACE, TFRC, PF4, IL23R, THBD, 2 10.00 38 185 SIRT1, F13A1, THPO, CDC42, F8, THBS1, CDH5, ENTPD1, SPARC, APP, EGR1, STAT4, SMAD4, ANGPT2, ACTN1, VWF, PDCD1, IL11, MMRN1 HAVCR2, IFNA1, PLG, FCGR2A, TNFSF13B, EPO, PTEN, IL17F, ELANE, GPT, ITGB1, SYK, IL1A, SERPINE1, HIF1A, F3, FCGR1A, LYN, 3 8.97 34 148 WDTC1, ENG, IL1RN, PECAM1, CD209, KITLG, CCR1, IL9, ERBB2, TBX21, IFNB1, KDR, ITGAL, NOS3, EDN1, APOE CPB2, F11, F12, CFH, MPL, SAA1, FGG, P4HB, OXGR1, IL12A, CASR, P2RY12, SERPINC1, PPBP, PTGER3, F7, GATA2, C3AR1, PROC, 4 8.16 26 104 GATA1, F9, LGALS1, SERPIND1, C4A, HABP2, ITGA2B

Table 4. Clusters of the disease target PPI network.

Te gene chip detected the gene expression trend in platelets. Under the action of justicidin D, the expression of 2392 genes were up-regulated, and the expression of 3268 genes were down-regulated (Fig. 9A). Comparing these signifcant expression genes with the 48 common targets in the network analysis, 21 genes were screened (Fig. 9B), and enrichment analysis was performed on these 21 genes (Table 6). Te GO results (the top 6 items are shown in Fig. 9C) show that the BP process involves cell migration (Fig. 9E, Te core regulatory part consists of APP, PTGS2, IL1B, IL6, and TNF), infammation, cell adhesion, and the production of infammatory factors. CC involves the receptor complex and membrane components, while MF involves the binding of enzyme bodies and cytokine receptors. KEGG enriches multiple signaling pathways including human cytomegalovirus infection, PI3K-Akt signaling pathway, pathways in cancer, human papillomavirus infection, and human cytomegalovirus infection (Fig. 9D). Tis indicates that justicidin D also afects the signaling pathways related to infection in the anti-thrombotic process, which is consistent with the anti-infammatory activity of Justicia.

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Figure 6. GO and KEGG enrichment analysis of 48 common targets. (A–C) are biological processes, cellular components, and molecular functions in GO analysis. D is the KEGG enrichment analysis item. All show the top 10.

Figure 7. GO and KEGG enrichment analysis of 9 hub targets. A and B represent the frst 10 entries enriched by GO and KEGG, respectively.

Justicidin D can resist platelet aggregation and prevent the lethal efect of pulmonary embo‑ lism. As shown in Fig. 10A, justicidin D has a signifcant inhibitory efect on ADP-induced platelet aggrega- tion (IC50 = 21.58 μM), which is more efective than the positive control drug aspirin (a widely known anti- platelet aggregation drug, Fig. 10B, IC50 = 69.16 μM). Even more surprising is that we observed the survival of pulmonary embolism mouse models within 30 min. As shown in Fig. 10C, justicidin D, like aspirin, can efec- tively reduce the mortality of pulmonary embolism mice (Ten mice in each group were included in the statistical analysis. All survived in the control group and all died in the model group. Te protection rate of justicidin D was 20%, and the protection rate of aspirin was 30%). By HE staining (Fig. 10D), it can be seen that the blood

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Figure 8. (A) 5 overlaps among the top 20 KEGG entries enriched by 48 common targets and 9 hub targets. (B) Te structure of justicidin D.

ID Pathway Log (P) Gene IDs hsa04151 PI3K-Akt signaling pathway − 10.466 AKT1, GSK3B, IL2, IL6, KDR, MET, PRKCA, RAC1, SYK, TLR2, VEGFA hsa05418 Fluid shear stress and atherosclerosis − 11.468 AKT1, IL1B, KDR, MMP2, MMP9, RAC1, TNF, TNFRSF1A, VEGFA AKT1, GSK3B, IL1B, IL6, PRKCA, PTGER3, PTGER4, PTGS2, RAC1, CXCL12, TNF, TNFRSF1A, VEGFA, F2, hsa05163 Human cytomegalovirus infection − 15.868 IL2, MET, MMP2, MMP9, TERT, SYK, P4HB, TLR2, CBS, ABCG2 hsa04010 MAPK signaling pathway − 9.852 AKT1, IL1B, PRKCA, RAC1, TNF, TNFRSF1A hsa04060 Cytokine-cytokine receptor interaction − 8.396 IL1B, IL2, IL6, KDR, MET, CXCL12, TNF, TNFRSF1A, VEGFA

Table 5. Five common pathways associated with candidate targets according to enrichment analysis based on KEGG.

cells in the pulmonary blood vessels of the blank group are loose, and the blood cells in the blood vessels of the model group are tightly packed and form thrombus. Under the intervention of justicidin D, the blood cells in the blood vessels are relatively normalized, which is also consistent with the efect of aspirin. Te results of animal experiments show that justicidin D has a good anti-thrombotic efect and is a potential anti-thrombotic compound. It also shows that Justicia is a Chinese herbal medicine with potential development and application value in the feld of cardiovascular diseases. Discussion Traditional Chinese medicine has protected the health of the Chinese people for thousands of years and has amazing curative efects. Unfortunately, the potential target of Chinese herbal medicine is difcult to determine, so far it has not yet received a high degree of international recognition. Te research strategy of network phar- macology provides a feasible path for the pharmacological research of Chinese medicine. In this study, we frst used network pharmacology to fnd important targets and potential active ingredients related to thrombosis in Justicia and predicted that multiple active ingredients would act on several proteins related to thrombosis. Ten, we selected the active ingredients for experiments to verify the results of the network analysis. In the analysis of core components, quercetin, apigenin, luteolin, cynaroside, scopoletin, palmitic acid, cleistanthin B, diphyllin, and justicidin D may be the active components with anti-thrombotic efect. Evidence has shown that quercetin can inhibit platelet activation in arterial thrombosis, and quercetin derivatives have become inhibitors of thrombosis­ 41,42. As a favonoid, apigenin has similar activity to quercetin and can inhibit platelet adhesion and thrombosis with ­aspirin43. Luteolin, as a favonoid in Ginkgo biloba extract, is a human

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Figure 9. Gene chip detection confrmed that justicidin D can cause the diferential expression of 21 genes out of 48 common genes. (A) Volcano plot, justicidin D caused high expression of 2392 genes and low expression of 3268 genes. (B) Among the 48 common genes in the network analysis, we have determined that 21 genes have signifcant diferential expression. Red is high expression and green is low expression. (C) GO enrichment entries for 21 diferentially expressed genes. (D) KEGG enrichment analysis showed the frst fve signaling pathways. (E) Te most important biological process enriched is the positive regulation of cell migration, which displays the diferentially expressed gene network, the core part of which is composed of APP, PTGS2, IL1B, IL6, and TNF.

thrombin inhibitor, which is widely used in the prevention and treatment of thrombosis and cardiovascular ­disease44. Cynaroside, a derivative of luteolin, can reduce oxidant-induced cardiomyocyte apoptosis­ 45. Scopoletin, a drug candidate for angiogenesis inhibitors, works by interrupting the autophosphorylation of VEGF receptor 2 (VEGFR2) and downstream signaling pathways, and can prevent steatosis and lower blood sugar­ 46. Te ratio of oleic acid to palmitic acid in the diet can determine the concentration of postprandial thrombosis and fbrinolytic factors in men­ 47. Tere is no direct evidence that cleistanthin B and diphyllin are related to thrombosis, but they have progressive efects in inducing cell apoptosis and improving obesity­ 48,49. Unfortunately, quercetin, apigenin, luteolin, cynaroside, scopoletin, palmitic acid, cleistanthin B, and diphyllin are not unique compounds in Justicia, but it is undeniable that they contribute to the anti-thrombotic efect of Justicia. Justicidin-, a unique lignan ingredient in Justicia, has been reported to have biological activity, and Justicidin D (also called neojusticin A, Fig. 8B) is one of them. Justicidin A, a well-defned arylnaphthalide lignan, has shown anti-cancer activity and can exert neuroprotective efects by inhibiting the hyperphosphorylation of the protein tau and regulating the activities of GSK-3β and AMPK­ 50. In addition, similar to Justicidin A, Justicidin B can selectively inhibit T helper 2 (T2) cell responses in concanavalin A-activated spleen cells and polarized T2 cells, thereby alleviating airway infammation and ­bronchoconstriction51. Justicidin C, also called neojusticin 52 B, has been reported to directly bind to integrin αIIbβ3 to inhibit platelet aggregation­ . Terefore, based on the results of network analysis and combined with literature reports, justicidin D was used as a key focus in this work for experimental research. F2, MMP9, CXCL12, MET, RAC1, PDE5A, and ABCB1 are genes regulated by justicidin D identifed in this work. Among them, F2, MMP9, CXCL12, and MET are also the seed nodes in the network cluster analysis and have been shown that they have a multi-dimensional connection with thrombosis. What is more, the activation

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Term Category Description LogP Symbols APP, F3, IL1B, IL6, KDR, MMP9, PRKCA, PTGS2, RAC1, CXCL12, GO:0,030,335 GO Biological Processes Positive regulation of cell migration − 16.978 SELP, TERT, TNF, TLR2, SYK GO:0,150,076 GO Biological Processes Neuroinfammatory response − 13.229 APP,IL1B,IL6,MMP9,PTGS2,TLR2,TNF,GBA,TERT,KDR,GSK3B GSK3B, IL1B, IL6, KDR, PRKCA, RAC1, CXCL12, SELP, SYK, TNF, GO:0,045,785 GO Biological Processes Positive regulation of cell adhesion − 12.771 PTGS2, MMP9, APP, F3, GBA, TLR2, TERT, ABCB1 F3, IL1B, IL6, SYK, TLR2, TNF, APP, GBA, PTGS2, KDR, PRKCA, GO:0,032,757 GO Biological Processes Positive regulation of interleukin-8 production − 11.553 RAC1, GSK3B, MMP9, SELP GO:0,050,900 GO Biological Processes Leukocyte migration − 10.3134 APP, GBA, IL6, MMP9, RAC1, CXCL12, SELP, SYK, TNF, KDR GO:0,043,235 GO Cellular Components Receptor complex − 4.394 APP, IL6, KDR, SYK, TLR2 GO:0,019,898 GO Cellular Components Extrinsic component of membrane − 2.846 GBA, RAC1, SYK GO:0,098,552 GO Cellular Components Side of membrane − 6.697 F3, ABCB1, RAC1, CXCL12, SELP, SYK, TNF, IL6, APP, IL1B GO:0,062,023 GO Cellular Components Collagen-containing extracellular matrix − 2.432 F3, MMP9, CXCL12 GO:0,045,121 GO Cellular Components Membrane raf − 5.457 APP, LTB4R, KDR, TLR2, TNF, TERT, SELP GO:0,005,126 GO Molecular Functions Cytokine receptor binding − 5.851 IL1B, IL6, CXCL12, SYK, TNF GO:0,004,672 GO Molecular Functions Protein kinase activity − 3.172 GSK3B, KDR, PRKCA, SYK GO:0,002,020 GO Molecular Functions Protease binding − 3.908 F3, GSK3B, TNF GO:0,005,178 GO Molecular Functions Integrin binding − 7.235 IL1B, KDR, PRKCA, CXCL12, SYK, APP, MMP9, ABCB1 GO:0,035,325 GO Molecular Functions Toll-like receptor binding − 4.385 SYK, TLR2 hsa05163 KEGG Pathway human Cytomegalovirus infection − 11.335 GSK3B, IL1B, IL6, PRKCA, PTGS2, RAC1, CXCL12, TNF hsa04151 KEGG Pathway PI3K-Akt signaling pathway − 8.115 GSK3B, IL6, KDR, PRKCA, RAC1, SYK, TLR2 GSK3B, IL6, MMP9, PRKCA, PTGS2, RAC1, CXCL12, TERT, TNF, hsa05200 KEGG Pathway Pathways in cancer − 8.324 ABCB1, SYK hsa05165 KEGG Pathway Human papillomavirus infection − 5.288 GSK3B, PRKCA, PTGS2, TERT, TNF, MMP9, ABCB1, APP, TUBB1 hsa05163 KEGG Pathway Human cytomegalovirus infection − 11.336 GSK3B, IL1B, IL6, PRKCA, PTGS2, RAC1, CXCL12, TNF

Table 6. Te GO and KEGG enrichment analysis of 21 validated genes (top 5).

and difusion of RAC1 under high arteries signifcantly reduces collagen adhesion and thrombus ­formation53,54. Consistent with this, the gene chip detection in this work also showed that RAC1 was signifcantly high expres- sion under the action of justicidin D (Fig. 10B). Under the intervention of justicidin D, the expression of PDE5A was up-regulated and the expression of ABCB1 was down-regulated. PDE5A can mediate and protect myocardial hypertrophy caused by hypertension and has a regulatory efect in the blood system­ 55. ABCB1 participates in the transport of drugs in the blood and mediates the drug resistance of tumors­ 56. In short, justicidin D inhibits thrombosis by regulating F2, MMP9, CXCL12, MET, RAC1, PDE5A, and ABCB1. In addition to infection-related signaling pathways, the PI3K-Akt signaling pathway is also enriched by com- mon targets (Supplementary Table S3) and 9 hub targets (Fig. 7B). More importantly, the diferential genes regu- lated by justicidin D are also enriched in the PI3K-Akt signaling pathway. Te diferential genes involved include GSK3B, IL6, KDR, PRKCA, RAC1, SYK, TLR2, PIK3CA, JAK1, PDK1, IRS1, MYC, PTEN, MAGI1 (Fig. 11A,B). As a key node in the PI3K/Akt signaling process, the expression of PIK3CA was found to be signifcantly down- regulated. However, what is puzzling is that Akt1, another key node, has not been detected with signifcant expression changes, and the relatively low throughput of gene chip detection may explain this phenomenon. However, we also doubt whether AKT1 is still at the core in the process of thrombosis. Te expanded conjecture is that the anti-thrombotic efect of justicidin D will not be mediated by AKT1. It is more likely that there is a gene downstream of PIK3CA that mediates this efect. Tis conjecture needs to be confrmed by in-depth experi- ments, but it is undeniable that PI3K-Akt signaling pathway plays a vital role in the anti-thrombotic process. Importantly, the PI3K-Akt signaling pathway is also a pathway where the predicted targets of the compounds in Justicia are commonly enriched. In-depth understanding is that the compounds in Justicia, including justicidin D, have anti-thrombotic efects through the PI3K-Akt signaling pathway. Conclusion In the present study, a network pharmacology combined with experimental research analysis strategy was estab- lished to reveal the anti-thrombotic mechanism of Justicia compounds. Te study found the anti-thrombotic targets, clusters, biological processes, and pathways of Justicia and confrmed that the core compounds in Justicia have antiplatelet aggregation efect and can reduce the mortality of mice with pulmonary embolism through experiments. At the same time, gene chip detection also confrmed that the characteristic components in Justicia can regulate the expression of thrombosis related genes.

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Figure 10. (A,B) represent the inhibitory efects of justicidin D and aspirin on platelet aggregation, respectively. (C) Te protective efect of justicidin D and aspirin on pulmonary embolism in mice (n = 10. All survived in the control group and all died in the model group. Te protection rate of justicidin D was 20%, and the protection rate of aspirin was 30%). (D) HE (400 ×) staining of lungs of mice with pulmonary embolism, (a) blank group, (b) model group, (c) justicidin D group, (d) aspirin group.

Figure 11. (A) Pathway mapping of the PI3K-Akt pathway. Green indicates down-regulation and red indicates up-regulation. (B) Te expression of related genes in the PI3K-Akt pathway detected by the gene chip.

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Data availability Te datasets used and/or analysed during the current study were available from the corresponding author on reasonable request. Te main supporting data can be found in the supplementary material of the article.

Received: 17 March 2021; Accepted: 13 August 2021

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Author contributions H.Z.C. and Z.T. are responsible for conducting experiments and writing manuscripts. Z.Y., X.Z.T., and L.Y. assist in experiments and data analysis. Yu.Y.F. assists in animal experiment. Ya.Y.F., W.H.Z., and L.B. are responsible for designing and guiding the experiment. Funding Tis research was supported by the Key Project of Science and Technology Research Program Department of Education of Hubei Provincial (D20192004).

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